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在Linux環(huán)境下，CMake是一個(gè)常用的構(gòu)建工具，它可以幫助我們自動(dòng)化構(gòu)建過(guò)程，提高開(kāi)發(fā)效率，對(duì)于并行計(jì)算應(yīng)用程序，我們需要特別關(guān)注一些配置技巧，以確保程序能夠正確地利用多核處理器進(jìn)行并行計(jì)算，本文將介紹一些使用CMake構(gòu)建Linux并行計(jì)算應(yīng)用程序的配置技巧。

1、啟用并行編譯

為了充分利用多核處理器進(jìn)行編譯，我們需要在CMakeLists.txt文件中啟用并行編譯，可以通過(guò)設(shè)置CMAKE_MAKE_PROGRAM變量為"make j${NUMBER_OF_PROCESSORS}"來(lái)實(shí)現(xiàn)。

set(CMAKE_MAKE_PROGRAM "make j${NUMBER_OF_PROCESSORS}")

NUMBER_OF_PROCESSORS可以通過(guò)get_processor_count()函數(shù)獲取系統(tǒng)的處理器數(shù)量。

2、啟用并行運(yùn)行測(cè)試

在執(zhí)行測(cè)試時(shí)，我們同樣希望能夠利用多核處理器進(jìn)行并行運(yùn)行，可以通過(guò)設(shè)置CMAKE_TEST_PARALLEL_WORKERS變量來(lái)實(shí)現(xiàn)。

set(CMAKE_TEST_PARALLEL_WORKERS ${NUMBER_OF_PROCESSORS})

3、啟用并行運(yùn)行程序

在運(yùn)行程序時(shí)，我們希望能夠利用多核處理器進(jìn)行并行運(yùn)行，可以通過(guò)設(shè)置CMAKE_BUILD_PARALLEL_LEVEL和CMAKE_RUN_PARALLEL_LEVEL變量來(lái)實(shí)現(xiàn)。

set(CMAKE_BUILD_PARALLEL_LEVEL ${NUMBER_OF_PROCESSORS})
set(CMAKE_RUN_PARALLEL_LEVEL ${NUMBER_OF_PROCESSORS})

4、使用OpenMP并行化代碼

為了實(shí)現(xiàn)真正的并行計(jì)算，我們需要在代碼中使用OpenMP庫(kù)來(lái)編寫(xiě)并行化的代碼，需要在CMakeLists.txt文件中包含OpenMP庫(kù)：

find_package(OpenMP)
if (OPENMP_FOUND)
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
    set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

在需要并行化的代碼段前后添加#pragma omp parallel for指令：

#include 
#include 
#include 
int main() {
    std::vector data(100);
    #pragma omp parallel for
    for (int i = 0; i < data.size(); ++i) {
        data[i] = i * 2;
    }
    for (int i = 0; i < data.size(); ++i) {
        std::cout << data[i] << std::endl;
    }
    return 0;
}

5、使用Intel TBB并行化代碼（可選）

除了OpenMP，我們還可以使用Intel TBB庫(kù)來(lái)實(shí)現(xiàn)并行計(jì)算，需要在CMakeLists.txt文件中包含TBB庫(kù)：

find_package(TBB)
if (TBB_FOUND)
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TBB_CXX_FLAGS}")
    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${TBB_C_FLAGS}")
    set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${TBB_LIBRARIES}")
endif()

在需要并行化的代碼段前后添加tbb::parallel_for指令：

#include 
#include 
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#include 
#include  // For printing benchmark results to the console using CUPS API. Only needed if you want to print benchmark results to the console. You can remove this include if not needed.// If you want to print benchmark results to the console, you need to install the CUPS library and enable its support in your CMake configuration.// For example, add the following lines to your CMakeLists.txt file: find_package(CUDA REQUIRED) target_link libraries(yourTargetName PRIVATE CUDA::CUDA) target link libraries(yourTargetName PRIVATE CUPSVG) target link libraries(yourTargetName PRIVATE CUPS) target link libraries(yourTargetName PRIVATE CUPSAPI) target link libraries(yourTargetName PRIVATE CUPSNET) target link libraries(yourTargetName PRIVATE CUPSZIP) target link libraries(yourTargetName PRIVATE CUPSPDF) target link libraries(yourTargetName PRIVATE CUPSSMTP) target link libraries(yourTargetName PRIVATE CUPSPOP3) target link libraries(yourTargetName PRIVATE CUPSIMAP4) target link libraries(yourTargetName PRIVATE CUPSPRINT)// Then, in your benchmark code, you can use the following function to print benchmark results to the console using the CUPS API: void printBenchmarkResultsToConsole() { timeval start, end; gettimeofday(&start, NULL); // Your benchmark code here... gettimeofday(&end, NULL); double elapsedTime = end.tv_sec start.tv

本文名稱(chēng)：使用CMake構(gòu)建Linux并行計(jì)算應(yīng)用程序的配置技巧
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